DEG-analysis

workflowr

• Summary
• Checks
• Past versions

Last updated: 2024-01-22

Checks: 7 0

Knit directory: UPF1-FMR1/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20230923)

The command set.seed(20230923) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 12a58fe

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 12a58fe. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Rproj.user/
    Ignored:    analysis/Exploratory-DEG.nb.html
    Ignored:    analysis/Quality-control.nb.html

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/DEG-analysis.Rmd) and HTML (docs/DEG-analysis.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	98b3ae9	urwahnawaz	2024-01-18	Build site.
Rmd	4f57b71	urwahnawaz	2024-01-18	wflow_publish(c("analysis/index.Rmd", "analysis/DEG-analysis.Rmd"))
html	2b88aaf	urwahnawaz	2024-01-17	Build site.
html	144afef	urwahnawaz	2024-01-17	Build site.
Rmd	068b751	urwahnawaz	2024-01-17	wflow_git_commit("./*")
html	facdd9d	urwahnawaz	2024-01-17	Build site.
html	6cb16fa	urwahnawaz	2024-01-17	Build site.
Rmd	e0ee067	urwahnawaz	2024-01-17	wflow_publish("analysis/DEG-analysis.Rmd")
html	2b5790f	urwahnawaz	2024-01-12	Build site.
Rmd	bd34cfd	urwahnawaz	2024-01-12	wflow_publish("analysis/DEG-analysis.Rmd")

---

Introduction

source(here::here("code/libraries.R"))
library(stargazer)
library(ggfortify)
library(glue)
library(cowplot)
library(broom)
library(glmpca)
library(naniar)
library(gridExtra)
library(EnsDb.Hsapiens.v86)
library(ggrepel)
library(org.Hs.eg.db)
library(msigdbr)
library(fgsea)

goSummaries <- url("https://uofabioinformaticshub.github.io/summaries2GO/data/goSummaries.RDS") %>%
    readRDS() %>%
    mutate(ontology = as.character(ontology))

getGeneLists <- function(pwf, goterms, genome, ids){
    gene2cat <- getgo(rownames(pwf), genome, ids)
    cat2gene <- split(rep(names(gene2cat), sapply(gene2cat, length)),
                      unlist(gene2cat, use.names = FALSE))
    out <- list()
    for(term in goterms){
        tmp <- pwf[cat2gene[[term]],]
        tmp <- rownames(tmp[tmp$DEgenes > 0, ])
        out[[term]] <- tmp
    }
    out


}

txdf = transcripts(EnsDb.Hsapiens.v86, return.type="DataFrame")
tx2gene = as.data.frame(txdf[,c("tx_id","gene_id", "tx_biotype")])

ah <- AnnotationHub() %>%
    subset(species == "Homo sapiens") %>%
    subset(rdataclass == "EnsDb") %>% 
  subset(genome == "GRCh38")

ensDb <- ah[["AH109606"]]
grTrans <- transcripts(ensDb)
trLengths <- exonsBy(ensDb, "tx") %>%
    width() %>%
    vapply(sum, integer(1))
mcols(grTrans)$length <- trLengths[names(grTrans)]

genesGR = genes(ensDb)
transGR = transcripts(ensDb)

mcols(transGR) = mcols(transGR) %>%
  cbind(
    transcriptLengths(ensDb)[rownames(.), c("nexon", "tx_len")]
  )


id2Name <- structure(
  genesGR$gene_name,
  names = genesGR$gene_id
) %>% 
  .[!duplicated(names(.))]

salmon.files = ("/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Salmon")
salmon = list.files(salmon.files, pattern = "transcripts$", full.names = TRUE)
all_files = file.path(salmon, "quant.sf")
sample_names = gsub("/home/neuro/Documents/NMD_analysis/Analysis/Results/UPF1-FMR1/Salmon/", "", salmon)
sample_names = gsub(".gz_transcripts", "", sample_names)
sample_names = gsub("\\_.*", "", sample_names)
names(all_files) <- sample_names
md = read.csv(here::here("data/Sample_info.csv"), header= TRUE) %>%  
  #mutate(files = file.path(salmon, "quant.sf")) %>% 
  dplyr::rename("names" = "GeneWiz.ID", 
                "Group" = "Sample.type") %>% 
  mutate(Group = ifelse(Group == "MC" | Group == "FC", "Control", Group)) %>%
  dplyr::select(names,everything()) %>% 
  mutate(names = gsub("\\_.*", "", names) )
md = md[order(match(md$names, sample_names)),]
md %<>% 
  rownames_to_column("random") %>% 
#  column_to_rownames("names") %>%
  dplyr::select(-random) %>%
  mutate(files = all_files)

md %<>% dplyr::filter(Group != "FMR1") %>%
 dplyr::filter(names != "23-LDJ6767") %>%
dplyr::filter(names != "202")
all_files = all_files[names(all_files) %in% md$names]

txi_genes =  tximport(all_files, type="salmon", txOut=FALSE,
                      countsFromAbundance="scaledTPM", tx2gene = tx2gene, ignoreTxVersion = TRUE, ignoreAfterBar = TRUE)
keep.genes = (rowSums(txi_genes$abundance >= 1 ) >= 3)

Differential gene expression analysis

txi_genes_filtered = txi_genes$counts[keep.genes,]
y <- DGEList(txi_genes_filtered)


design <- model.matrix(~Batch +Group + Sex, data = md) %>% 
    set_colnames(gsub(pattern = "Group", replacement ="", x = colnames(.)))

y <- calcNormFactors(y)
v <- voom(y, design)

fit = lmFit(v, design) %>% 
    eBayes()

summary(decideTests(fit, lfc =0))

       (Intercept) Batch  FRAX  UPF1  SexM
Down           398   439   533   233    11
NotSig        2189 12093 11814 12551 13189
Up           10627   682   867   430    14

UPF1 DEGs

upf1_results_lfc = topTable(fit,coef = "UPF1", number = Inf) %>% 
                     mutate(res = ifelse(logFC > 0& adj.P.Val < 0.05, "Upregulated", 
                      ifelse(logFC < 0 & adj.P.Val < 0.05, "Downregulated", "NotSig"))) %>% 
  rownames_to_column("ensembl_gene_id") %>%
   mutate(SYMBOL = mapIds(org.Hs.eg.db, keys=ensembl_gene_id,  column="SYMBOL",keytype="ENSEMBL", multiVals="first")) %>%
  mutate(res = ifelse(logFC > 0 & adj.P.Val < 0.05, "Upregulated", 
                      ifelse(logFC < 0  & adj.P.Val < 0.05, "Downregulated", "NotSig")))

DEColours <- c("Downregulated" = "#2e294e","Upregulated" = "#720026",  "NotSig" = "#E5E5E5")


volc_upf1 = upf1_results_lfc  %>%
    ggplot(aes(y = -log10(adj.P.Val), 
               x =  logFC , 
               colour = res,
               size =-log10(adj.P.Val), 
               label= SYMBOL)) +
  geom_point(alpha = 0.8) +
  # geom_text(aes(label=ifelse(SYMBOL== "Upf1",as.character(SYMBOL),''))) +
  #  geom_text(aes(label= SYMBOL), subset = SYMBOL == "Upf1") +
    scale_colour_manual(values = DEColours) + theme_classic() + 
    theme(axis.title.y = element_text(size = 12)) +
    geom_hline(yintercept = -log10(0.05), color = "grey60", size = 0.5, lty = "dashed") +
    labs(x = "log2 Fold Change", y = "-log10 adj p-value") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") +
  xlim(-8.5, 8.5) + ylim(0, 7.5)


# volc = volc_upf1+ geom_text_repel(data=subset(upf1_results_lfc , SYMBOL %in% c("UPF1", "UPF2", "UPF3B",
#                       "SMG5", "SMG6",
#                       "UPF3A", "ATF4", 
#                       "GADD5G")),
#             aes(label=SYMBOL),   position=position_dodge(width = 0.9), 
#              vjust=-0.40, color = "black", box.padding = 0.5, fill = "white") + ggtitle("DEGs in UPF1 relative to controls using limma/voom")
# 
# 
# volc 

my_gg = volc_upf1 + geom_point_interactive(aes(tooltip =SYMBOL, data_id = SYMBOL), 
    size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)

Volcano plot showing distribution of differentially expressed genes using the limma/voom pipeline

upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>% 
  dplyr::arrange(adj.P.Val) %>% 
  DT::datatable(caption="DEGS in UPF1 relative to controls using limma/voom pipeline") 

FRAX DEGs

frax_results_lfc = topTable(fit,coef = "FRAX", number = Inf) %>% 
                     mutate(res = ifelse(logFC > 0  & adj.P.Val < 0.05, "Upregulated", 
                      ifelse(logFC < 0 & adj.P.Val < 0.05, "Downregulated", "NotSig"))) %>% 
  rownames_to_column("ensembl_gene_id") %>%
   mutate(SYMBOL = mapIds(org.Hs.eg.db, keys=ensembl_gene_id,  column="SYMBOL",keytype="ENSEMBL", multiVals="first")) %>% 
    mutate(res = ifelse(logFC > 0 & adj.P.Val < 0.05, "Upregulated", 
                      ifelse(logFC < 0  & adj.P.Val < 0.05, "Downregulated", "NotSig"))) 

DEColours <- c("Downregulated" = "#2e294e","Upregulated" = "#720026",  "NotSig" = "#E5E5E5")


volc_frax = frax_results_lfc  %>%
    ggplot(aes(y = -log10(adj.P.Val), 
               x =  logFC , 
               colour = res,
               size =-log10(adj.P.Val), 
               label= SYMBOL)) +
  geom_point(alpha = 0.8) +
  # geom_text(aes(label=ifelse(SYMBOL== "Upf1",as.character(SYMBOL),''))) +
  #  geom_text(aes(label= SYMBOL), subset = SYMBOL == "Upf1") +
    scale_colour_manual(values = DEColours) + theme_classic() + 
    theme(axis.title.y = element_text(size = 12)) +
    geom_hline(yintercept = -log10(0.05), color = "grey60", size = 0.5, lty = "dashed") +
    labs(x = "log2 Fold Change", y = "-log10 adj p-value") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") +
  xlim(-8.5, 8.5) + ylim(0, 7.5)


# volc = volc + geom_text_repel(data=subset(upf1_results_lfc , SYMBOL %in% c("UPF1", "UPF2", "UPF3B",
#                       "SMG5", "SMG6",
#                       "UPF3A", "ATF4", 
#                       "GADD5G", "FMR1")),
#             aes(label=SYMBOL),   position=position_dodge(width = 0.9), 
#              vjust=-0.40, color = "black", box.padding = 0.5, fill = "white") + ggtitle("DEGs in FRAX relative to controls using limma/voom pipeline")


my_gg =  volc_frax + geom_point_interactive(aes(tooltip =SYMBOL, data_id = SYMBOL), 
    size = 1, hover_nearest = TRUE)
girafe(ggobj = my_gg)

Overlap of UPF1 and FRAX

• Number of genes overlapping

library(VennDiagram)
x= list("UPF1 DEGs" = upf1_results_lfc$ensembl_gene_id[upf1_results_lfc$adj.P.Val < 0.05], 
        "FRAX DEGs" = frax_results_lfc$ensembl_gene_id[frax_results_lfc$adj.P.Val < 0.05])

ggvenn(x)

Past versions of unnamed-chunk-13-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

overlaps = calculate.overlap(x)
upf1_only = overlaps$a1[!overlaps$a1 %in% overlaps$a3]
fmr1_only = overlaps$a2[!overlaps$a2 %in% overlaps$a3]

upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(frax_results_lfc %>% 
               dplyr::filter(adj.P.Val < 0.05) %>%
                dplyr::select(ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val), by = "ensembl_gene_id") %>% 
  ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("FRAX (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 222 overlapping genes")

Overlap of significant genes from FRAX and UPF1

Past versions of unnamed-chunk-15-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

• Gene ontology

genes = as.integer(upf1_results_lfc$ensembl_gene_id %in% overlaps$a3)
not_na = !is.na(genes)
names(genes) = upf1_results_lfc$ensembl_gene_id
genes = genes[not_na]
    
pwf=nullp(genes,plot.fit = FALSE, 'hg19','ensGene')
Go.wall=goseq(pwf, 'hg19','ensGene', use_genes_without_cat = TRUE)
goterms <- Go.wall$category
goList <- getGeneLists(pwf, goterms, 'hg19','ensGene')
Go.wall$EnsemblID <- sapply(Go.wall$category, function(x) goList[[x]])
Go.wall$EnsemblID <- vapply(Go.wall$EnsemblID, paste, collapse = ", ", character(1L))

goRes = Go.wall %>%
            dplyr::select(-under_represented_pvalue) %>%
            dplyr::filter(numDEInCat > 0) %>%
            dplyr::rename(id = category) %>%
            left_join(goSummaries)
nGenes <- c(BG = sum(genes == 0), DE = sum(genes))
goFinal <- goRes %>%
  dplyr::filter(numDEInCat > 2) %>% 
  dplyr::filter(shortest_path > 3) %>%
  arrange(desc(shortest_path), EnsemblID) %>% 
  distinct(EnsemblID, .keep_all = TRUE) %>%
  arrange(over_represented_pvalue) %>% 
  mutate(Expected = round(nGenes[["DE"]] * numInCat / nGenes[["BG"]], 0)) %>%
            mutate(adjP = p.adjust(over_represented_pvalue, "bonferroni"),
                   FDR = p.adjust(over_represented_pvalue, "fdr"),
                   Sig_adjP = adjP < 0.05, Sig_FDR = FDR < 0.05) %>%
            dplyr::filter(Sig_adjP, numDEInCat > Expected) %>%
  dplyr::select(term, everything()) 
  
  
goFinal %>% 
  DT::datatable(caption="222 overlapping genes")

• UPF1 specific genes

upf1_results_lfc %>% 
  dplyr::filter(ensembl_gene_id %in% upf1_only) %>% 
  dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(frax_results_lfc %>% 
                dplyr::select(ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val), by = "ensembl_gene_id") %>%
  ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("FRAX (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 441 UPF1 specific genes")

Past versions of unnamed-chunk-18-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

genes = as.integer(upf1_results_lfc$ensembl_gene_id %in% upf1_only)
not_na = !is.na(genes)
names(genes) = upf1_results_lfc$ensembl_gene_id
genes = genes[not_na]
    
pwf=nullp(genes,plot.fit = FALSE, 'hg19','ensGene')
Go.wall=goseq(pwf, 'hg19','ensGene', use_genes_without_cat = TRUE)
goterms <- Go.wall$category
goList <- getGeneLists(pwf, goterms, 'hg19','ensGene')
Go.wall$EnsemblID <- sapply(Go.wall$category, function(x) goList[[x]])
Go.wall$EnsemblID <- vapply(Go.wall$EnsemblID, paste, collapse = ", ", character(1L))

goRes = Go.wall %>%
            dplyr::select(-under_represented_pvalue) %>%
            dplyr::filter(numDEInCat > 0) %>%
            dplyr::rename(id = category) %>%
            left_join(goSummaries)
nGenes <- c(BG = sum(genes == 0), DE = sum(genes))
goFinal <- goRes %>%
  dplyr::filter(numDEInCat > 2) %>% 
  dplyr::filter(shortest_path > 4) %>%
  arrange(desc(shortest_path), EnsemblID) %>% 
  distinct(EnsemblID, .keep_all = TRUE) %>%
  arrange(over_represented_pvalue) %>% 
  mutate(Expected = round(nGenes[["DE"]] * numInCat / nGenes[["BG"]], 0)) %>%
            mutate(adjP = p.adjust(over_represented_pvalue, "bonferroni"),
                   FDR = p.adjust(over_represented_pvalue, "fdr"),
                   Sig_adjP = adjP < 0.05, Sig_FDR = FDR < 0.05) %>%
            dplyr::filter(Sig_adjP, numDEInCat > Expected) %>%
  dplyr::select(term, everything()) 
  
  
goFinal %>% 
  DT::datatable(caption="UPF1 specific genes")

• FMR1 specific genes

frax_results_lfc %>% 
  dplyr::filter(ensembl_gene_id %in% fmr1_only) %>% 
  dplyr::select(SYMBOL,ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val) %>% 
  inner_join(upf1_results_lfc %>% 
                dplyr::select(ensembl_gene_id, logFC, 
                            adj.P.Val), by = "ensembl_gene_id") %>%
  ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line", 
              size=5, alpha =0.6, 
              conf.int = TRUE, add.params = list(color = "#EF3829",
                                                 fill = "lightgray")) + 
    theme_bw() + ylab("FRAX (log2FoldChange)") + xlab("UPF1 (log2FoldChange)") +
  geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + ggtitle("Direction of expression of the 1178 FRAX specific genes")

Past versions of unnamed-chunk-21-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

Gene ontology

genes = as.integer(frax_results_lfc$ensembl_gene_id %in% fmr1_only)
not_na = !is.na(genes)
names(genes) = frax_results_lfc$ensembl_gene_id
genes = genes[not_na]
    
pwf=nullp(genes,plot.fit = FALSE, 'hg19','ensGene')
Go.wall=goseq(pwf, 'hg19','ensGene', use_genes_without_cat = TRUE)
goterms <- Go.wall$category
goList <- getGeneLists(pwf, goterms, 'hg19','ensGene')
Go.wall$EnsemblID <- sapply(Go.wall$category, function(x) goList[[x]])
Go.wall$EnsemblID <- vapply(Go.wall$EnsemblID, paste, collapse = ", ", character(1L))

goRes = Go.wall %>%
            dplyr::select(-under_represented_pvalue) %>%
            dplyr::filter(numDEInCat > 0) %>%
            dplyr::rename(id = category) %>%
            left_join(goSummaries)
nGenes <- c(BG = sum(genes == 0), DE = sum(genes))
goFinal <- goRes %>%
  dplyr::filter(numDEInCat > 2) %>% 
  dplyr::filter(shortest_path > 4) %>%
  arrange(desc(shortest_path), EnsemblID) %>% 
  distinct(EnsemblID, .keep_all = TRUE) %>%
  arrange(over_represented_pvalue) %>% 
  mutate(Expected = round(nGenes[["DE"]] * numInCat / nGenes[["BG"]], 0)) %>%
            mutate(adjP = p.adjust(over_represented_pvalue, "bonferroni"),
                   FDR = p.adjust(over_represented_pvalue, "fdr"),
                   Sig_adjP = adjP < 0.05, Sig_FDR = FDR < 0.05) %>%
            dplyr::filter(Sig_adjP, numDEInCat > Expected) %>%
  dplyr::select(term, everything()) 
  
  
goFinal %>% 
  DT::datatable(caption="FRAX specific genes")

Comparison of DEGs with DSB genes from Chakraborty et al

dsbs = read_xlsx(here::here("data/lists/DSBs/FX_DSB_genes.xlsx"), skip = 1)
exp_cl = read_xlsx(here::here("data/lists/DSBs/msrd_results_LCL_Silent_Genes.xlsx"), sheet = 1)

dsbs_lcls = dsbs[!dsbs$ensembl_gene_id %in% exp_cl$ensembl_id,]

• Jozef’s questions

• Is there an overlap of genes with DSBs in FRAXA/FMR1 LCLs and the DEGs in FRAXA/FMR1 LCLs even if across 2x different studies? If not overlap based on p value, is there a trend for these DSB-associated genes to be downregulated? *Is there an overlap of genes with DSBs in FRAXA/FMR1 LCLs and DEGs in UPF3B patient LCLs, the reason being that UPF3B patients have no or very little FMRP left as Saba determined? Also, if no overlap strictly on p value, is there a trend of the DSB genes to be down-regulated in UPF3B LCLs?

• The same as above, but comparing the DSB genes to UPF1 DEGs, etc.

• From this study - using the FRAX DEGs and UPF1 DEGs

x= list("UPF1 DEGs" = upf1_results_lfc$ensembl_gene_id[upf1_results_lfc$adj.P.Val < 0.05], 
        "FRAX DEGs" = frax_results_lfc$ensembl_gene_id[frax_results_lfc$adj.P.Val < 0.05], 
        "Double stranded breaks" = dsbs$ensembl_gene_id)

ggvenn(x)

Past versions of unnamed-chunk-25-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

frax_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  mutate(condition="FRAX") %>% 
  inner_join(dsbs, by = "ensembl_gene_id") %>% 
  rbind(upf1_results_lfc %>% 
          dplyr::filter(adj.P.Val < 0.05) %>%
          mutate(condition="UPF1") %>% 
  inner_join(dsbs, by = "ensembl_gene_id")) %>%
  ggplot(aes(x=condition, fill = res), color = "white") + geom_bar(stat = "count") + 
  theme_bw() + scale_fill_manual(values =c("Upregulated"=  "#720026", 
                                           "Downregulated"= "#2e294e")) + 
  ggtitle("Number of DEGs with DSBs")

Genes with DSBs identified in significantly expressed DEGs

Past versions of unnamed-chunk-26-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

library(ggstatsplot)
frax_results_lfc %>% 
  mutate(res = ifelse(logFC > 0, "Upregulated", 
                      ifelse(logFC < 0, "Downregulated", "NotSig"))) %>%
  mutate(condition="FRAX") %>% 
  inner_join(dsbs, by = "ensembl_gene_id") %>% 
  rbind(upf1_results_lfc %>% 
         mutate(res = ifelse(logFC > 0, "Upregulated", 
                      ifelse(logFC < 0, "Downregulated", "NotSig"))) %>%
          mutate(condition="UPF1") %>% 
  inner_join(dsbs, by = "ensembl_gene_id")) %>%
  ggbarstats(
  x                = res,
  y                = condition,

xlab = "",
legend.title     = "",
  ggplot.component = list(ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge = 2))),
) + scale_fill_manual(values =c("Upregulated"=  "#720026", 
                                           "Downregulated"= "#2e294e"))

Genes with DSBs identified in significantly expressed DEGs

Past versions of unnamed-chunk-27-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

 # ggplot(aes(x=condition, fill = res), color = "white") + geom_bar(stat = "count") + 
  #theme_bw() + scale_fill_manual(values =c("Upregulated"=  "#720026", 
             #                              "Downregulated"= "#2e294e")) + 
  #ggtitle("Number of DEGs with DSBs")

library(GeneOverlap)
enrichment_gene_list_pvalue = list()
enrichment_gene_list_OR = list()

test.list = list()
test.list$BG = frax_results_lfc$ensembl_gene_id
test.list$frax_sig = frax_results_lfc$ensembl_gene_id[frax_results_lfc$adj.P.Val < 0.05]
test.list$frax_sig_up = frax_results_lfc$ensembl_gene_id[frax_results_lfc$adj.P.Val < 0.05 & frax_results_lfc$logFC > 0]
test.list$frax_sig_down = frax_results_lfc$ensembl_gene_id[frax_results_lfc$adj.P.Val < 0.05 & frax_results_lfc$logFC < 0]
test.list$upf1_sig = upf1_results_lfc$ensembl_gene_id[upf1_results_lfc$adj.P.Val < 0.05]
test.list$upf1_sig_up = upf1_results_lfc$ensembl_gene_id[upf1_results_lfc$adj.P.Val < 0.05 & upf1_results_lfc$logFC > 0]
test.list$upf1_sig_down = upf1_results_lfc$ensembl_gene_id[upf1_results_lfc$adj.P.Val < 0.05 & upf1_results_lfc$logFC < 0]
test.list$dsbs = dsbs$ensembl_gene_id
## Maybe an exact test with odds ratio 

for (list in c("frax_sig","frax_sig_up","frax_sig_down","upf1_sig",
               "upf1_sig_up", "upf1_sig_down")) {
    go.obj <- newGeneOverlap(test.list[[list]],
                         test.list$dsbs,
                         genome.size=length(test.list$BG))
    go.obj <- testGeneOverlap(go.obj)
    enrichment_gene_list_pvalue[[paste0(list)]] = go.obj@pval
    enrichment_gene_list_OR[[paste0(list)]] = go.obj@odds.ratio
    
  }


enrichment_GL_pval = do.call(rbind, enrichment_gene_list_pvalue) %>% as.data.frame()
colnames(enrichment_GL_pval)[1] = c("pvalue")
enrichment_GL_pval$padj = p.adjust(enrichment_GL_pval$pvalue, method = "BH")
enrichment_GL_OR  = do.call(rbind, enrichment_gene_list_OR) %>% as.data.frame()
colnames(enrichment_GL_OR)[1] = c("OR")
GL_enrichment = cbind(enrichment_GL_pval, enrichment_GL_OR)

GL_enrichment %>% 
  rownames_to_column("DEGs") %>%
  ggplot(aes(x=OR, y = DEGs)) + geom_bar(stat = "identity", fill = "#edafb8") +
  theme_bw() + geom_vline(xintercept = 1) + 
   theme(axis.text.x = element_text(size = 12, family = "serif", color = "black"),
            axis.text.y = element_text(size = 12, family = "serif", color = "black"),
        axis.title = element_text(size=12, family = "serif"), 
         legend.box.background = element_rect(color = "black"),
          legend.text = element_text(family = "serif"), 
          legend.title = element_text( family = "serif"),
           plot.title = element_text(family = "serif", size =20),         
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_line( size=.1 ),
              legend.position = "top",
        strip.text.y = element_text(
        size = 10, face = "bold.italic", family = "serif"
        ),
         strip.text.x = element_text(
        size = 10, family = "serif"
        )) + labs(x="Odds ratio") + ggtitle("Enrichment of DSBs in UPF1 or FRAX")

Past versions of unnamed-chunk-29-1.png

Version	Author	Date
6cb16fa	urwahnawaz	2024-01-17

List of genes with DSBs

frax_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  mutate(condition="FRAX") %>% 
  inner_join(dsbs, by = "ensembl_gene_id") %>%
  dplyr::select(gene = SYMBOL, Expression = res, everything()) %>%
  DT::datatable(caption = "Genes in FRAX DEGs with DSBs")

upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  mutate(condition="FRAX") %>% 
  inner_join(dsbs, by = "ensembl_gene_id") %>%
  dplyr::select(gene = SYMBOL, Expression = res, everything()) %>%
  DT::datatable(caption = "Genes in UPF1 DEGs with DSBs")

Expression overlap with genes with DSBs

library(ggside)
upf1_results_lfc %>% 
  dplyr::filter(adj.P.Val < 0.05) %>%
  dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(frax_results_lfc %>% 
               dplyr::filter(adj.P.Val < 0.05) %>%
                dplyr::select(ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val), by = "ensembl_gene_id") %>% 
  mutate(DSBs = ifelse(ensembl_gene_id %in% dsbs$ensembl_gene_id, "DSBs", "No DSBs" )) %>%
 ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line",
              conf.int = TRUE, color = "DSBs", add.params = list(color = "blue",
                                                 fill = "lightgray"), alpha =0.8, shape = "DSBs", size =3) + 
    theme_bw() + xlab("UPF1 (log2FC)") +
    scale_color_manual(values = c("#F15A2B", "#2E368F")) + 
    geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + theme(legend.position = "none") +
  theme(axis.text.x = element_text(size = 12, family = "serif", color = "black"),
            axis.text.y = element_text(size = 12, family = "serif", color = "black"),
        axis.title = element_text(size=15, family = "serif"), 
         legend.box.background = element_rect(color = "black"),
          legend.text = element_text(family = "serif"), 
          legend.title = element_text( family = "serif"),
           plot.title = element_text(family = "serif", size =20),         
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_line( size=.1 ),
              legend.position = "top",
        strip.text.y = element_text(
        size = 10, face = "bold.italic", family = "serif"
        ),
         strip.text.x = element_text(
        size = 10, family = "serif"
        )) + labs(color = "Expression") + geom_xsideboxplot(aes(y = DSBs, fill= DSBs), orientation = "y") +
   theme(ggside.panel.scale = .3) +
  scale_xsidey_discrete()  + geom_ysidedensity(aes(x = after_stat(density), fill = DSBs), alpha = 0.5) +
  scale_color_manual(values = c("DSBs" = "#b23a48",
                                "No DSBs" = "grey")) +  scale_fill_manual(values = c("DSBs" = "#b23a48",
                                "No DSBs" = "grey")) + scale_shape_manual(values = c(19,15)) + labs(y= "FRAX (log2FC)")

Past versions of unnamed-chunk-32-1.png

Version	Author	Date
98b3ae9	urwahnawaz	2024-01-18

upf1_results_lfc %>% 
  dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(frax_results_lfc  %>%
                dplyr::select(ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val), by = "ensembl_gene_id") %>% 
  mutate(DSBs = ifelse(ensembl_gene_id %in% dsbs$ensembl_gene_id, "DSBs", "No DSBs" )) %>%
 ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line",
              conf.int = TRUE, color = "DSBs", add.params = list(color = "blue",
                                                 fill = "lightgray"), alpha =0.8, shape = "DSBs", size =3) + 
    theme_bw() + xlab("UPF1 (log2FC)") +
    scale_color_manual(values = c("#F15A2B", "#2E368F")) + 
    geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + theme(legend.position = "none") +
  theme(axis.text.x = element_text(size = 12, family = "serif", color = "black"),
            axis.text.y = element_text(size = 12, family = "serif", color = "black"),
        axis.title = element_text(size=15, family = "serif"), 
         legend.box.background = element_rect(color = "black"),
          legend.text = element_text(family = "serif"), 
          legend.title = element_text( family = "serif"),
           plot.title = element_text(family = "serif", size =20),         
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_line( size=.1 ),
              legend.position = "top",
        strip.text.y = element_text(
        size = 10, face = "bold.italic", family = "serif"
        ),
         strip.text.x = element_text(
        size = 10, family = "serif"
        )) + labs(color = "Expression") + geom_xsideboxplot(aes(y = DSBs, fill= DSBs), orientation = "y") +
   theme(ggside.panel.scale = .3) +
  scale_xsidey_discrete()  + geom_ysidedensity(aes(x = after_stat(density), fill = DSBs), alpha = 0.5) +
  scale_color_manual(values = c("DSBs" = "#b23a48",
                                "No DSBs" = "grey")) +  scale_fill_manual(values = c("DSBs" = "#b23a48",
                                "No DSBs" = "grey")) + scale_shape_manual(values = c(19,15)) + labs(y= "FRAX (log2FC)")

Past versions of unnamed-chunk-33-1.png

Version	Author	Date
98b3ae9	urwahnawaz	2024-01-18

frax_results_lfc %>% 
  dplyr::filter(ensembl_gene_id %in% fmr1_only) %>% 
  dplyr::select(SYMBOL,ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val) %>% 
  inner_join(upf1_results_lfc %>% 
                dplyr::select(ensembl_gene_id, logFC, 
                            adj.P.Val), by = "ensembl_gene_id") %>% 
  mutate(DSBs = ifelse(ensembl_gene_id %in% dsbs$ensembl_gene_id, "DSBs", "No DSBs" )) %>%
 ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line",
              conf.int = TRUE, color = "DSBs", add.params = list(color = "blue",
                                                 fill = "lightgray"), alpha =0.8, shape = "DSBs", size =3) + 
    theme_bw() + xlab("UPF1 (log2FC)") +
    scale_color_manual(values = c("#F15A2B", "#2E368F")) + 
    geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + theme(legend.position = "none") +
  theme(axis.text.x = element_text(size = 12, family = "serif", color = "black"),
            axis.text.y = element_text(size = 12, family = "serif", color = "black"),
        axis.title = element_text(size=15, family = "serif"), 
         legend.box.background = element_rect(color = "black"),
          legend.text = element_text(family = "serif"), 
          legend.title = element_text( family = "serif"),
           plot.title = element_text(family = "serif", size =20),         
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_line( size=.1 ),
              legend.position = "top",
        strip.text.y = element_text(
        size = 10, face = "bold.italic", family = "serif"
        ),
         strip.text.x = element_text(
        size = 10, family = "serif"
        )) + labs(color = "Expression") + geom_xsideboxplot(aes(y = DSBs, fill= DSBs), orientation = "y") +
   theme(ggside.panel.scale = .3) +
  scale_xsidey_discrete()  + geom_ysidedensity(aes(x = after_stat(density), fill = DSBs), alpha = 0.5) +
  scale_color_manual(values = c("DSBs" = "#b23a48",
                                "No DSBs" = "grey")) +  scale_fill_manual(values = c("DSBs" = "#b23a48",
                                "No DSBs" = "grey")) + scale_shape_manual(values = c(19,15)) + labs(y= "FRAX (log2FC)")

Past versions of unnamed-chunk-34-1.png

Version	Author	Date
98b3ae9	urwahnawaz	2024-01-18

upf1_results_lfc %>% 
  dplyr::filter(ensembl_gene_id %in% upf1_only) %>% 
  dplyr::select(SYMBOL,ensembl_gene_id, logFC, adj.P.Val) %>% 
  inner_join(frax_results_lfc %>% 
                dplyr::select(ensembl_gene_id, FRAX.logFC=logFC, 
                              FRAX.padj =adj.P.Val), by = "ensembl_gene_id") %>% 
  mutate(DSBs = ifelse(ensembl_gene_id %in% dsbs$ensembl_gene_id, "DSBs", "No DSBs" )) %>%
 ggscatter(., x="logFC", y = "FRAX.logFC", cor.coef = TRUE, add = "reg.line",
              conf.int = TRUE, color = "DSBs", add.params = list(color = "blue",
                                                 fill = "lightgray"), alpha =0.8, shape = "DSBs", size =3) + 
    theme_bw() + xlab("UPF1 (log2FC)") +
    scale_color_manual(values = c("#F15A2B", "#2E368F")) + 
    geom_hline(yintercept = 0, size = 0.5,lty = "dashed", color = "grey60") +
  geom_vline(xintercept = 0, size = 0.5, lty = "dashed", color = "grey60") + theme(legend.position = "none") +
  theme(axis.text.x = element_text(size = 12, family = "serif", color = "black"),
            axis.text.y = element_text(size = 12, family = "serif", color = "black"),
        axis.title = element_text(size=15, family = "serif"), 
         legend.box.background = element_rect(color = "black"),
          legend.text = element_text(family = "serif"), 
          legend.title = element_text( family = "serif"),
           plot.title = element_text(family = "serif", size =20),         
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_line( size=.1 ),
              legend.position = "top",
        strip.text.y = element_text(
        size = 10, face = "bold.italic", family = "serif"
        ),
         strip.text.x = element_text(
        size = 10, family = "serif"
        )) + labs(color = "Expression") + geom_xsideboxplot(aes(y = DSBs, fill= DSBs), orientation = "y") +
   theme(ggside.panel.scale = .3) +
  scale_xsidey_discrete()  + geom_ysidedensity(aes(x = after_stat(density), fill = DSBs), alpha = 0.5) +
  scale_color_manual(values = c("DSBs" = "#b23a48",
                                "No DSBs" = "grey")) +  scale_fill_manual(values = c("DSBs" = "#b23a48",
                                "No DSBs" = "grey")) + scale_shape_manual(values = c(19,15)) + labs(y= "FRAX (log2FC)")

Past versions of unnamed-chunk-35-1.png

Version	Author	Date
98b3ae9	urwahnawaz	2024-01-18

Data export

Session information

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

time zone: Australia/Adelaide
tzcode source: system (glibc)

attached base packages:
 [1] grid      stats4    tools     stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] ggside_0.2.3                GeneOverlap_1.38.0         
 [3] ggstatsplot_0.12.1          VennDiagram_1.7.3          
 [5] futile.logger_1.4.3         fgsea_1.28.0               
 [7] msigdbr_7.5.1               org.Hs.eg.db_3.18.0        
 [9] ggrepel_0.9.4               EnsDb.Hsapiens.v86_2.99.0  
[11] gridExtra_2.3               naniar_1.0.0               
[13] glmpca_0.2.0                broom_1.0.5                
[15] cowplot_1.1.1               glue_1.6.2                 
[17] ggfortify_0.4.16            stargazer_5.2.3            
[19] ngsReports_2.4.0            patchwork_1.1.3            
[21] AnnotationHub_3.10.0        BiocFileCache_2.10.1       
[23] dbplyr_2.4.0                openxlsx_4.2.5.2           
[25] ggiraph_0.8.8               DT_0.31                    
[27] msigdb_1.10.0               GSEABase_1.64.0            
[29] graph_1.80.0                annotate_1.80.0            
[31] XML_3.99-0.16               pheatmap_1.0.12            
[33] ggvenn_0.1.10               MetBrewer_0.2.0            
[35] ggpubr_0.6.0                venn_1.11                  
[37] viridis_0.6.4               viridisLite_0.4.2          
[39] tximeta_1.20.1              tximport_1.30.0            
[41] goseq_1.54.0                geneLenDataBase_1.38.0     
[43] BiasedUrn_2.0.11            org.Mm.eg.db_3.18.0        
[45] EnsDb.Mmusculus.v79_2.99.0  ensembldb_2.26.0           
[47] AnnotationFilter_1.26.0     GenomicFeatures_1.54.1     
[49] AnnotationDbi_1.64.1        biomaRt_2.58.0             
[51] edgeR_4.0.3                 limma_3.58.1               
[53] DESeq2_1.42.0               SummarizedExperiment_1.32.0
[55] Biobase_2.62.0              MatrixGenerics_1.14.0      
[57] matrixStats_1.2.0           GenomicRanges_1.54.1       
[59] GenomeInfoDb_1.38.2         IRanges_2.36.0             
[61] S4Vectors_0.40.2            BiocGenerics_0.48.1        
[63] corrplot_0.92               lubridate_1.9.3            
[65] forcats_1.0.0               purrr_1.0.2                
[67] readr_2.1.4                 tidyverse_2.0.0            
[69] stringr_1.5.1               tidyr_1.3.0                
[71] scales_1.3.0                data.table_1.14.10         
[73] readxl_1.4.3                tibble_3.2.1               
[75] magrittr_2.0.3              reshape2_1.4.4             
[77] ggplot2_3.4.4               dplyr_1.1.4                
[79] workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] fs_1.6.3                      ProtGenerics_1.34.0          
  [3] bitops_1.0-7                  insight_0.19.7               
  [5] httr_1.4.7                    RColorBrewer_1.1-3           
  [7] backports_1.4.1               utf8_1.2.4                   
  [9] R6_2.5.1                      statsExpressions_1.5.2       
 [11] lazyeval_0.2.2                mgcv_1.9-0                   
 [13] withr_2.5.2                   prettyunits_1.2.0            
 [15] cli_3.6.2                     formatR_1.14                 
 [17] sandwich_3.1-0                labeling_0.4.3               
 [19] sass_0.4.8                    mvtnorm_1.2-4                
 [21] pbapply_1.7-2                 Rsamtools_2.18.0             
 [23] systemfonts_1.0.5             rstudioapi_0.15.0            
 [25] RSQLite_2.3.4                 generics_0.1.3               
 [27] BiocIO_1.12.0                 gtools_3.9.5                 
 [29] crosstalk_1.2.1               vroom_1.6.5                  
 [31] car_3.1-2                     zip_2.3.0                    
 [33] GO.db_3.18.0                  Matrix_1.6-4                 
 [35] fansi_1.0.6                   abind_1.4-5                  
 [37] lifecycle_1.0.4               multcomp_1.4-25              
 [39] whisker_0.4.1                 yaml_2.3.8                   
 [41] carData_3.0-5                 gplots_3.1.3                 
 [43] SparseArray_1.2.2             paletteer_1.5.0              
 [45] blob_1.2.4                    promises_1.2.1               
 [47] crayon_1.5.2                  lattice_0.22-5               
 [49] KEGGREST_1.42.0               zeallot_0.1.0                
 [51] pillar_1.9.0                  knitr_1.45                   
 [53] rjson_0.2.21                  estimability_1.4.1           
 [55] admisc_0.34                   codetools_0.2-19             
 [57] fastmatch_1.1-4               getPass_0.2-4                
 [59] vctrs_0.6.5                   png_0.1-8                    
 [61] cellranger_1.1.0              gtable_0.3.4                 
 [63] rematch2_2.1.2                datawizard_0.9.0             
 [65] cachem_1.0.8                  xfun_0.41                    
 [67] S4Arrays_1.2.0                mime_0.12                    
 [69] correlation_0.8.4             coda_0.19-4                  
 [71] survival_3.5-7                statmod_1.5.0                
 [73] TH.data_1.1-2                 interactiveDisplayBase_1.40.0
 [75] ellipsis_0.3.2                nlme_3.1-164                 
 [77] bit64_4.0.5                   progress_1.2.3               
 [79] filelock_1.0.3                rprojroot_2.0.4              
 [81] bslib_0.6.1                   KernSmooth_2.23-22           
 [83] colorspace_2.1-0              DBI_1.1.3                    
 [85] tidyselect_1.2.0              processx_3.8.3               
 [87] emmeans_1.8.9                 bit_4.0.5                    
 [89] compiler_4.3.2                curl_5.2.0                   
 [91] git2r_0.33.0                  xml2_1.3.6                   
 [93] ggdendro_0.1.23               DelayedArray_0.28.0          
 [95] plotly_4.10.3                 bayestestR_0.13.1            
 [97] rtracklayer_1.62.0            caTools_1.18.2               
 [99] callr_3.7.3                   rappdirs_0.3.3               
[101] digest_0.6.33                 rmarkdown_2.25               
[103] XVector_0.42.0                htmltools_0.5.7              
[105] pkgconfig_2.0.3               highr_0.10                   
[107] fastmap_1.1.1                 rlang_1.1.2                  
[109] htmlwidgets_1.6.4             shiny_1.8.0                  
[111] farver_2.1.1                  jquerylib_0.1.4              
[113] zoo_1.8-12                    jsonlite_1.8.8               
[115] BiocParallel_1.36.0           RCurl_1.98-1.13              
[117] GenomeInfoDbData_1.2.11       parameters_0.21.3            
[119] munsell_0.5.0                 Rcpp_1.0.11                  
[121] babelgene_22.9                visdat_0.6.0                 
[123] stringi_1.8.3                 zlibbioc_1.48.0              
[125] MASS_7.3-60                   plyr_1.8.9                   
[127] parallel_4.3.2                Biostrings_2.70.1            
[129] splines_4.3.2                 pander_0.6.5                 
[131] hms_1.1.3                     locfit_1.5-9.8               
[133] ps_1.7.5                      uuid_1.1-1                   
[135] ggsignif_0.6.4                effectsize_0.8.6             
[137] futile.options_1.0.1          BiocVersion_3.18.1           
[139] evaluate_0.23                 lambda.r_1.2.4               
[141] BiocManager_1.30.22           tzdb_0.4.0                   
[143] httpuv_1.6.13                 MatrixModels_0.5-3           
[145] BayesFactor_0.9.12-4.6        xtable_1.8-4                 
[147] restfulr_0.0.15               rstatix_0.7.2                
[149] later_1.3.2                   memoise_2.0.1                
[151] GenomicAlignments_1.38.0      timechange_0.2.0             
[153] here_1.0.1